Self-organizing maps
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
A fast learning algorithm for deep belief nets
Neural Computation
Two topographic maps for data visualisation
Data Mining and Knowledge Discovery
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Generalized concentration/inequality indices of economic systems evolving in time
WSEAS Transactions on Mathematics
Estimating spatial interaction models using panel data: a generalized maximum entropy formulation
MCBE'10/MCBC'10 Proceedings of the 11th WSEAS international conference on mathematics and computers in business and economics and 11th WSEAS international conference on Biology and chemistry
Multi-layer topology preserving mapping for K-means clustering
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
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We investigate the use of the new Cross Entropy method as a tool for exploratory data analysis. We show how this method can be used to perform linear projections such as principal component analysis, exploratory projection pursuit and canonical correlation analysis. We further go on to show how topology preserving mappings can be created usin the cross entropy method. We also show how the cross entropy method can be used to train deep architecture nets which are one of the main current research directions for creating true artificial intelligence. Finally we show how the cross entropy method can be used to optimize parameters for latent variable models.